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Machine Learning
This page contains resources about Pattern Recognition, Computational Statistics and Machine Learning in general. More specific information is included in each subfield. Subfields and Concepts See Category:Machine Learning for some of its subfields. * Supervised Learning ** Classification *** Discriminative Model Vs Generative Model ** Regression *** Parametric Model Vs Nonparametric Model ** Structured Learning ** Generalized Linear Model (GLM or GLIM) ** Support Vector Machine ** Supervised Dimensionality Reduction ** Adaptive Basis Function Model *** Decision Tree Learning **** Classification and Regression Tree (CART) **** ID3 Algorithm *** Artificial Neural Network **** Feedforward Neural Network **** Recurrent Neural Network **** Radial Basis Function (RBF) Network **** Kohonen Network *** Supervised Ensemble Learning **** Bayesian Averaging **** Bagging **** Boosting **** Bayes Optimal Classifier **** Decision Forest / Random Forest ** Supervised Dictionary Learning ** Supervised Deep Learning *** Deep Belief Network * Unsupervised Learning ** Clustering / Discrete Latent Variable Models ** Unsupervised Dimensionality Reduction / Continuous Latent Variable Models *** Manifold Learning (although supervised variants exist) *** Autoencoder ** Unsupervised Ensemble Learning ** Unsupervised Dictionary Learning ** Unsupervised Deep Learning *** Deep Autoencoder *** Deep Belief Network * Semi-supervised Learning ** Active Learning * Inductive Learning ** Supervised Learning ** Semi-supervised Learning * Analytical Learning * Instance-based Learning ** Lazy Learning *** k-Nearest Neighbors (k-NN) Algorithm *** Case-based Reasoning (CBR) ** Eager Lerning *** RBF Network *** Kernel Machine *** Decision Tree Learning *** Backpropagation *** Naive Bayes * Reinforcement Learning ** Multi-Armed Bandit ** Finite Markov Decision Process ** Temporal-Difference Learning ** Q-Learning ** Adaptive Dynamic Programming ** Deep Reinforcement Learning * Probabilistic Machine Learning ** Bayesian Network (directed graphical models) ** Markov Random Field (undirected graphical models) ** Mixture Model ** Stochastic Model * Bayesian Machine Learning ** Variational Bayesian Learning * Statistical Learning Theory and Computational Learning Theory ** Online Learning and Sequential Prediction * Applications ** Computer Vision ** Medical Imaging ** Robotics ** Natural Language Processing ** Computational Finance ** Bioinformatics Online Courses Video Lectures * Machine Learning by Andrew Ng - Coursera * Machine Learning by Pedro Domingos - Coursera * Neural Networks for Machine Learning by Geoffrey Hinton - Coursera * Practical Machine Learning by Jeff Leek - Coursera * NYU Course on Big Data, Large Scale Machine Learning by John Langford and Yann LeCun * Learning from Data by Yaser Abu-Mostafa * Introduction to Machine Learning by Barnabas Poczos and Alex Smola * Machine Learning by Nando de Freitas * Pattern Recognition by Fred A. Hamprecht (2011 2012 ) * Machine Learning and Pattern Recognition by Charles Sutton (Lecture notes ) * Machine Learning by Joachim M. Buhmann * Pattern Recognition by P.S.Sastry - NPTEL * CS188 Intro to AI by Pieter Abbeel - Edx * PASCAL Lecture Series - VideoLectures.Net Lecture Notes Introductory * CO395: Machine Learning by Maja Pantic - very introductory course * Prediction: Machine Learning and Statistics by Cynthia Rudin- very introductory course * Machine Learning by Michael Littman * SGN-2506: Introduction to Pattern Recognition by Jussi Tohka * CSCI1950-F: Introduction to Machine Learning by Erik Sudderth * CS 229: Machine Learning by Andrew Ng * CSC 411: Machine Learning and Data Mining by Aaron Hertzmann * CS 760: Machine Learning by David Page * CSE446: Machine Learning * Introduction To Machine Learning by David Sontag * CSC321: Introduction to Neural Networks and Machine Learning by Tijmen Tieleman - this might be a bit advanced for beginners * CSC2515: Introduction to Machine Learning by Geoffrey Hinton - this is very similar to the above * Introduction to Pattern Recognition by Sargur Srihari * Introduction to Machine Learning Course by Sargur Srihari - this might be a bit advanced for beginners * Introduction to Machine Learning by Shai Shalev-Shwartz - this might be a bit advanced for beginners * COS 511: Foundations of Machine Learning by Rob Schapire * CS 2750 Machine Learning by Milos Hauskrecht * Introductory Applied Machine Learning by Victor Lavrenko and Nigel Goddard * Machine Learning and Pattern Recognition by Yann LeCun * Machine Learning by Tom Mitchell * Machine Learning by Tommi Jaakkola * Machine Learning by Andrew Zisserman * Pattern Recognition and Analysis by Rosalind W. Picard * CSCE 666: Pattern Analysis Fall by Ricardo Gutierrez-Osuna * Pattern Recognition by Richard Zanibbi * Neural Networks and Pattern Recognition by Ömer Cengiz ÇELEBİ * Machine Learning by Carl Edward Rasmussen and Zoubin Ghahramani * Learning from Data by Amos Storkey * Machine Learning: Pattern Recognition by Gwenn Englebienne * Machine Learning by Tony Jebara * Machine Learning I by Le Song Advanced * CS281: Advanced Machine Learning by Ryan Adams * CSC2535: Advanced Machine Learning by Geoffrey Hinton * Advanced Topics in Machine Learning by Andreas Krause * Advanced Topics in Machine Learning (Kernel Methods) by Arthur Gretton - Gatsby * Pattern Recognition by Ricardo Gutierrez-Osuna * Introduction to Pattern Recognition by Jason Corso * Pattern Recognition by Olga Veksler * Pattern Recognition by Charles Robertson * Pattern Recognition by Esa Alhoniemi * Advanced Machine Learning by Mehryar Mohri * Advanced Machine Learning by Tony Jebara * Machine Learning II by Le Song Specialized * STA 663 Statistical Computing and Computation by Cliburn Chan and Janice McCarthy * Statistical Machine Learning from Data by Samy Bengio * STA 4273H: Statistical Machine Learning by Ruslan Salakhutdinov * CS59000: Statistical Machine Learning by Alan Qi * Statistical Machine Learning and Data Mining by Yee Whye Teh * Identification, Estimation, and Learning by Harry Asada * Unsupervised Learning by Zoubin Ghahramani * Probabilistic and Unsupervised Learning by Maneesh Sahani - Gatsby * Approximate Inference and Learning in Probabilistic Models by Maneesh Sahani - Gatsby * Reinforcement Learning by David Silver * Reinforcement Learning by Michael Herrmann * Pattern Recognition by Xia Hong * Pattern Recognition for Machine Vision by Bernd Heisele and Yuri Ivanov * CS 5785: Modern Analytics by Serge J. Belongie * Advanced Statistical Machine Learning by Stefanos Zafeiriou Books Practical * Witten, I. H., & Frank, E. (2005). Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann. * Martinez, W. L., & Martinez, A. R. (2007). Computational statistics handbook with MATLAB. 2nd Ed. CRC press. * Martinez, W. L., Martinez, A. R., Martinez, A., & Solka, J. (2010). Exploratory data analysis with MATLAB. 2nd Ed. CRC Press. * Sammut, C., & Webb, G. I. (Eds.). (2011). Encyclopedia of Machine Learning. Springer Science & Business Media. * Han, J., Pei, J., & Kamber, M. (2011). Data mining: concepts and techniques. 3rd Ed. Morgan Kaufmann. * Conway, D., & White, J. (2012). Machine Learning for Hackers. O'Reilly Media. * McKinney, W. (2012). Python for data analysis: Data wrangling with Pandas, NumPy, and IPython. O'Reilly Media. * Rajaraman, A., & Ullman, J. D. (2012). Mining of Massive Datasets. Cambridge University Press. * Brownlee, J. (2013). Clever Algorithms: Statistical Machine Learning Recipes. Jason Brownlee. * Schutt, R., & O'Neil, C. (2013). Doing data science: Straight talk from the frontline. " O'Reilly Media, Inc.". * Battiti, R., & Brunato, M. (2014). The LION Way. Machine Learning Plus Intelligent Optimization. CreateSpace. * Zumel, N., Mount, J., & Porzak, J. (2014). Practical data science with R. Manning. * Nolan, D., & Lang, D. T. (2015). Data Science in R: A Case Studies Approach to Computational Reasoning and Problem Solving. CRC Press. * Davidson-Pilon, C. (2015). Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference. Addison-Wesley Professional. * Elston, S. F. (2015). Data Science in the Cloud with Microsoft Azure Machine Learning and R. O'Reilly Media, Inc. * Kelleher, J. D., Mac Namee, B., & D'Arcy, A. (2015). Fundamentals of machine learning for predictive data analytics: algorithms, worked examples, and case studies. MIT Press. * Lantz, B. (2015). Machine Learning with R. 2nd Ed. Packt Publishing Ltd. * Yu-Wei, C. D. C. (2015). Machine Learning with R cookbook. Packt Publishing Ltd. * Raschka, S. (2015). Python Machine Learning. Packt Publishing Ltd. * Ankan, A., & Panda, A. (2015). Mastering Probabilistic Graphical Models Using Python. Packt Publishing Ltd. * Grus, J. (2015). Data Science from Scratch: First Principles with Python. O'Reilly Media. * Madhavan, S. (2015). Mastering Python for Data Science. Packt Publishing Ltd. * Zaccone, G. (2016). Getting started with TensorFlow. Packt Publishing Ltd. * VanderPlas, J. (2016). Python Data Science Handbook: Essential Tools for Working with Data. O'Reilly Media. * Müller, A. C., & Guido, S. (2016). Introduction to Machine Learning with Python: A Guide for Data Scientists. O'Reilly Media, Inc. * Wickham, H., & Grolemund, G. (2017). R for Data Science. O'Reilly Media. * Geron, A. (2017). Hands-on machine learning with Scikit-Learn and TensorFlow: concepts, tools, and techniques to build intelligent systems.''O'Reilly Media. Introductory * Mitchell, T. M. (1997). ''Machine Learning. McGraw Hill. * Smola, A., & Vishwanathan, S. V. N. (2008). Introduction to Machine Learning. Cambridge University Press. * Alpaydin, E. (2010). Introduction to Machine Learning. MIT Press. * Theodoridis, S., Pikrakis, A., Koutroumbas, K., & Cavouras, D. (2010). Introduction to Pattern Recognition: A Matlab Approach. Academic Press. * Abu-Mostafa, Y. S., Magdon-Ismail, M., & Lin, H. T. (2012). Learning From Data. AMLBook. * Kuhn, M., & Johnson, K. (2013). Applied Predictive Modeling. Springer. * Kruse, R., Borgelt, C., Klawonn, F., Moewes, C., Steinbrecher, M., & Held, P. (2013). Computational Intelligence: A Methodological Introduction. Springer Science & Business Media. * James, G., Witten, D., & Hastie, T. (2014). An Introduction to Statistical Learning: With Applications in R. * Aggarwal, C. C. (2015). Data Mining: The Textbook. Springer. * Blum, A., Hopcroft, J., & Kannan, R. (2015). Foundations of Data Science. * Nilsson, N. J. (2015). Introduction to machine learning. An early draft of a proposed textbook. Advanced * Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer. * Theodoridis, S., Koutroumbas, K., (2009). Pattern Recognition, 4th Ed., Academic Press * Duda, R. O., Hart, P. E., & Stork, D. G. (2012). Pattern Classification. John Wiley & Sons. * Mohri, M., Rostamizadeh, A., & Talwalkar, A. (2012). Foundations of Machine Learning. MIT press. * Shalev-Shwartz, S., & Ben-David, S. (2014). Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press. * Theodoridis, S. (2015). Machine Learning: A Bayesian and Optimization Perspective. Academic Press. Specialized * Rojas, R. (1996). Neural Networks: A Systematic Introduction. Springer Science & Business Media. * Moon, T. K., & Stirling, W. C. (2000). Mathematical methods and algorithms for signal processing. Pearson. * Webb, A. R. (2002). Statistical Pattern Recognition. 2nd Ed. John Wiley & Sons. * MacKay, D. J. (2003). Information Theory, Inference and Learning Algorithms. Cambridge University Press. * Kushner, H., & Yin, G. G. (2003). Stochastic Approximation and Recursive Algorithms and Applications (Vol. 35). 2nd Ed. Springer Science & Business Media. * Taylor,J. S. & Cristianini, N. (2004). Kernel Methods for Pattern Analysis. Cambridge University Press. * Williams, C. K., & Rasmussen, C. E. (2006). Gaussian Processes for Machine Learning. MIT Press. * Vapnik, V. (2006). Estimation of dependences based on empirical data. Springer Science & Business Media. * Hastie, T., Tibshirani, R., Friedman, J., Hastie, T., Friedman, J., & Tibshirani, R. (2009). The Elements of Statistical Learning. 2nd Ed. New York: Springer. * Koller, D., & Friedman, N. (2009). Probabilistic Graphical Models. MIT Press. * Haykin, S. O. (2009). Neural Networks and Learning Machines. 3rd Ed. Pearson. * Gentle, J. E. (2009). Computational statistics. Springer. * Russell, S. J., & Norvig, P. (2010). "Part IV: Uncertain knowledge and reasoning". Artificial Intelligence: A Modern Approach. Prentice Hall. * Bühlmann, P., & Van De Geer, S. (2011). Statistics for High-Dimensional Data: Methods, Theory and Applications. Springer Science & Business Media. * Givens, G. H., & Hoeting, J. A. (2012). Computational statistics. 2nd Ed. John Wiley & Sons. * Murphy, K. P. (2012). Machine Learning: A Probabilistic Perspective. MIT Press. * Barber, D. (2012). Bayesian Reasoning and Machine Learning. Cambridge University Press. * Bubeck, S. & Cesa-Bianchi, N. (2012). Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems. Foundations and Trends® in Machine Learning, 5(1), 1-122. Now Publishers. * Jebara, T. (2012). Machine Learning: Discriminative and Generative (Vol. 755). Springer Science & Business Media. * Nielsen, M. A. (2015). Neural Networks and Deep Learning. Determination Press. * Goodman, N. D., & Tenenbaum, J. B. (2016). Probabilistic Models of Cognition. ''2nd Ed. (link) * Bengio, Y., Goodfellow, I. J., & Courville, A. (2016). ''Deep Learning. MIT Press. Software * PRMLT - MATLAB Toolbox for the book of PRML by C. Bishop * pmtk3 - Probabilistic Modeling Toolkit for MLPP book by Murphy in Matlab/Octave (3rd edition) * pyprobml - Python code for MLPP book by K. Murphy * BRMLtoolbox - MATLAB and Julia code for the BRML book by D. Barber * PyBRML - Python code for the BRML book by D. Barber * PythonDataScienceHandbook - Python code for the PDSH book by by J. VanderPlas * OpenML * Microsoft R * BigML - Prediction and Analytics tasks under 16MB are free * Neural Network Toolbox - MATLAB * Torch7 - a scientific computing framework for Machine Learning algorithms (based on Lua) * Lush - an OOP language for large-scale numerical and graphic applications (based on Lisp) * TensorFlow - Google * CNTK - Microsoft * Pylearn2 - A Machine Learning research library * scikit-learn - Python * mlpy - Python * Orange - Data Visualization and Analysis * Matlab Machine Learning Toolboxes * mloss.org. Machine Learning open source software * April-ANN - A Pattern Recognizer In Lua with ANNs * Weka- Data mining software in Java * MLTK - Machine Learning Toolkit in Java * OpenNN * Bayesian Modeling and Monte Carlo Methods * The Lightspeed Matlab Toolbox * MCML - broad range support for Monte Carlo methods to implement Machine Learning applications * Orange - Visual programming language * MLPACK - C++ * Shogun - C++ toolbox that offers interfaces for MATLAB,Octave, Python, R, Java, Lua, Ruby and C#(mainly for Kernel Machines) * autograd - Efficiently computes derivatives of numpy code (Python) * pySPACE - Signal Processing And Classification Environment (SPACE) in Python * dlib - C++ (with Python API) * Computational Statistics Toolbox - MATLAB * Exploratory Data Analysis (EDA) Toolbox - MATLAB * aimacode - Code for the AIMA book by Russell and Norvig * Core ML - Apple Datasets * UCI Machine Learning Repository - a large collection of standard datasets for testing learning algorithms * DeepLearning.Net - a list of datasets that can be used for benchmarking Deep Learning algorithms * MLdata See also *System Identification / Estimation Theory *Optimization *Information Theory *Predictive Learning vs. Representation Learning Other Resources * Artificial Intelligence - Google Scholar Metrics (Top Publications) * Computer Vision and Pattern Recognition - Google Scholar Metrics (Top Publications) * Data Mining and Analysis - Google Scholar Metrics (Top Publications) * NIPS - A top-tier Conference in Machine Learning * ICML - A top-tier Conference in Machine Learning *Machine Learning Types - Medium *Video Tutorials - Youtube channel of 'Mathematical Monk' *Basic Concepts in Machine Learning * Awesome-Machine-Learning (Github) - A curated list of Machine Learning frameworks, libraries and software (by language) * Computational Statistics in Python (2016 version, Github) *Comparison of software toolkits *Software for Data Mining, Analytics, Data Science, and Knowledge Discovery - KDnuggets *Machine Learning and Statistical Learning in R * Metacademy - List of concepts in Machine Learning * Machine Learning, Statistical Inference and Induction - Notebook * A Course in Machine Learning by Hal Daumé III - textbook * Courses on Statistical Pattern Recognition - summary of 33 courses * FastML - List of Machine Learning courses online * Results and Errors Percentage on Standard Datasets - MNIST, CIFAR, Pascal VOC, etc. * Machine Learning Surveys - List of literature surveys, reviews, and tutorials on Machine Learning and related topics * Machine Learning on Google+ - online community * The Shape of Data - Data Mining and Machine Learning blog * Data Mining & Machine Learning - a mindmap diagram comparing the two areas * Basic Glossary of Machine Learning * Machine Learning with MATLAB - free ebook * References for Machine Learning - Wikia * Which machine learning algorithm should I use? - blog post Category:Machine Learning Category:Artificial Intelligence Category:Algorithms